Influences of a Covariate on Growth Mixture Modeling

Abstract

This study investigated the influence of including a covariate and/or a distal outcome on growth mixture modeling (GMM). GMM was used to examine patterns of days of heroin use over 16 years among 471 heroin users and the relationship of those patterns to mortality (distal outcome). Comparisons were made among four types of models: without a covariate and a distal outcome (two-stage approach), with a distal outcome, with a covariate, and with a covariate and a distal outcome in conjunction with three different covariates. The two-stage approach and models with the inclusion of a distal outcome resulted in different conclusions when testing the impact of latent trajectory membership on the distal outcome. Differences in membership classifications between unconditional and conditional models were mainly determined by two factors: (1) the associations of the trajectories with the covariate and the distal outcome, and (2) the distribution of the covariate in the study sample.

Figure 1

The Four Sets of Growth Mixture Models Examined in the Study

Figure 2. The Four Heroin-use Trajectories Identified by the Unconditional Model (Model 1)